Metadata-Version: 2.1
Name: midv500
Version: 0.2.0
Summary: Download and convert MIDV-500 annotations to COCO instance segmentation format
Home-page: https://github.com/fcakyon/midv500
Author: Fatih Cagatay Akyon
Author-email: 
License: UNKNOWN
Description: [![Downloads](https://pepy.tech/badge/midv500)](https://pepy.tech/project/midv500)
        [![PyPI version](https://badge.fury.io/py/midv500.svg)](https://badge.fury.io/py/midv500)
        ![CI](https://github.com/fcakyon/midv500/workflows/CI/badge.svg)
        
        ## Download and convert MIDV-500 datasets into COCO instance segmentation format
        Automatically download/unzip [MIDV-500](https://arxiv.org/abs/1807.05786) and [MIDV-2019](https://arxiv.org/abs/1910.04009) datasets and convert the annotations into COCO instance segmentation format.
        
        Then, dataset can be directly used in the training of Yolact, Detectron type of models.
        
        ## MIDV-500 Datasets
        MIDV-500 consists of 500 video clips for 50 different identity document types including 17 ID cards, 14 passports, 13 driving licences and 6 other identity documents of different countries with ground truth which allows to perform research in a wide scope of various document analysis problems. Additionally, MIDV-2019 dataset contains distorted and low light images in it.
        
        <img width="1000" alt="teaser" src="./figures/midv500.png">
        
        You can find more detail on papers:
        
        [MIDV-500: A Dataset for Identity Documents Analysis and Recognition on Mobile Devices in Video Stream](https://arxiv.org/abs/1807.05786)
        
        [MIDV-2019: Challenges of the modern mobile-based document OCR](https://arxiv.org/abs/1910.04009)
        
        
        ## Getting started
        ### Installation
        ```console
        pip install midv500
        ```
        
        ### Usage
        
        - Import package:
        
        ```python
        import midv500
        ```
        
        - Download and unzip desired version of the dataset:
        
        ```python
        # set directory for dataset to be downloaded
        dataset_dir = 'midv500_data/'
        
        # download and unzip the base midv500 dataset
        dataset_name = "midv500"
        midv500.download_dataset(dataset_dir, dataset_name)
        
        # or download and unzip the midv2019 dataset that includes low light images
        dataset_name = "midv2019"
        midv500.download_dataset(dataset_dir, dataset_name)
        
        # or download and unzip both midv500 and midv2019 datasets
        dataset_name = "all"
        midv500.download_dataset(dataset_dir, dataset_name)
        ```
        
        - Convert downloaded dataset to coco format:
        
        ```python
        # set directory for coco annotations to be saved
        export_dir = 'midv500_data/'
        
        # set the desired name of the coco file, coco file will be exported as "filename + '_coco.json'"
        filename = 'midv500'
        
        # convert midv500 annotations to coco format
        midv500.convert_to_coco(dataset_dir, export_dir, filename)
        ```
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.5
Description-Content-Type: text/markdown
